A Hybrid Deep Learning and Model-Checking Framework for Accurate Brain Tumor Detection and Validation

• URL: http://arxiv.org/abs/2501.01991v1
• Date: Tue, 31 Dec 2024 08:09:08 GMT
• Title: A Hybrid Deep Learning and Model-Checking Framework for Accurate Brain Tumor Detection and Validation
• Authors: Lahcen El Fatimi, Elhoucine Elfatimi, Hanifa Bouchaneb,
• Abstract summary: This paper introduces a novel hybrid framework integrating model checking with deep learning for brain tumor detection and validation in medical imaging. Experimental results highlight the framework's effectiveness, achieving 98% accuracy, 96.15% precision, and 100% recall.
• Score: 0.0
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• Abstract: Model checking, a formal verification technique, ensures systems meet predefined requirements, playing a crucial role in minimizing errors and enhancing quality during development. This paper introduces a novel hybrid framework integrating model checking with deep learning for brain tumor detection and validation in medical imaging. By combining model-checking principles with CNN-based feature extraction and K-FCM clustering for segmentation, the proposed approach enhances the reliability of tumor detection and segmentation. Experimental results highlight the framework's effectiveness, achieving 98\% accuracy, 96.15\% precision, and 100\% recall, demonstrating its potential as a robust tool for advanced medical image analysis.

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